Re: [Numpy-discussion] question about long doubles on ppc64el

[Matthew Brett]

Hi,

On Sun, Jan 15, 2017 at 10:00 PM, Thomas Caswell  wrote:
> Folks,
>
> Over at h5py we are trying to get a release out and have discovered (via
> debian) that on ppc64el there is an apparent disagreement between the size
> of a native long double according to hdf5 and numpy.
>
> For all of the gorey details see: https://github.com/h5py/h5py/issues/817 .
>
> In short, `np.longdouble` seems to be `np.float128` and according to the
> docs should map to the native 'long double'.  However, hdf5 provides a
> `H5T_NATIVE_LDOUBLE` which should also refer to the native 'long double',
> but seems to be a 64 bit float.
>
> Anyone on this list have a ppc64el machine (or experience with) that can
> provide some guidance here?

I know that long double on numpy for the PPC on Mac G4 (power64 arch)
is the twin double, as expected, so I'd be surprised if that wasn't
true for numpy on ppc64el .

Do you want a login for the G4 running Jessie?   If so, send me your
public key off-list?

Cheers,

Matthew
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Re: [Numpy-discussion] question about long doubles on ppc64el

[Charles R Harris]

On Sun, Jan 15, 2017 at 11:00 PM, Thomas Caswell  wrote:

> Folks,
>
> Over at h5py we are trying to get a release out and have discovered (via
> debian) that on ppc64el there is an apparent disagreement between the size
> of a native long double according to hdf5 and numpy.
>
> For all of the gorey details see: https://github.com/h5py/h5py/issues/817
>  .
>
> In short, `np.longdouble` seems to be `np.float128` and according to the
> docs should map to the native 'long double'.  However, hdf5 provides a
> `H5T_NATIVE_LDOUBLE` which should also refer to the native 'long double',
> but seems to be a 64 bit float.
>
> Anyone on this list have a ppc64el machine (or experience with) that can
> provide some guidance here?
>

I believe the ppc64 long double is IBM double double, i.e., two doubles for
128 bits. It isn't IEEE compliant and probably not very portable. It is
possible that different compilers could treat it differently or it may be
flagged to be treated in some specific way.

Chuck
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[Numpy-discussion] GSoC 2017: NumFocus will be an umbrella organization

[Max Linke]

Hi

Organizations can start submitting applications for Google Summer of Code
2017 on January 19 (and the deadline is February 9)

https://developers.google.com/open-source/gsoc/timeline?hl=en

NumFOCUS will be applying again this year. If you want to work with us
please let me know and if you apply as an organization yourself or under a
different umbrella organization please tell me as well. If you participate
with us it would be great if you start to add possible projects to the
ideas page on github soon. We some general information for mentors on
github.

https://github.com/numfocus/gsoc/blob/master/CONTRIBUTING-mentors.md

We also have a template for ideas that might help. It lists the things
Google likes to see.

https://github.com/numfocus/gsoc/blob/master/2017/ideas-list-skeleton.md

In case you participated in earlier years with NumFOCUS there are some
small changes this year. Raniere won't be the admin this year. Instead I'm
going to be the admin. We are also planning to include two explicit rules
when a student should be failed, they have to communicate regularly and
commit code into your development branch at the end of the summer.

best, Max


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Re: [Numpy-discussion] NumPy 1.12.0 release

[Ralf Gommers]

On Mon, Jan 16, 2017 at 12:43 PM, Charles R Harris <
charlesr.har...@gmail.com> wrote:

> Hi All,
>
> I'm pleased to announce the NumPy 1.12.0 release. This release supports
> Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be
> downloaded from PiPY
> , the tarball
> and zip files may be downloaded from Github
> . The release notes
> and files hashes may also be found at Github
>  .
>
> NumPy 1.12.0rc 2 is the result of 418 pull requests submitted by 139
> contributors and comprises a large number of fixes and improvements. Among
> the many improvements it is difficult to  pick out just a few as standing
> above the others, but the following may be of particular interest or
> indicate areas likely to have future consequences.
>
> * Order of operations in ``np.einsum`` can now be optimized for large
> speed improvements.
> * New ``signature`` argument to ``np.vectorize`` for vectorizing with core
> dimensions.
> * The ``keepdims`` argument was added to many functions.
> * New context manager for testing warnings
> * Support for BLIS in numpy.distutils
> * Much improved support for PyPy (not yet finished)
>

Thanks for all the heavy lifting on this one Chuck!

Ralf
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Re: [Numpy-discussion] question about long doubles on ppc64el

[Jens Nielsen]

According to
https://docs.scipy.org/doc/numpy-dev/user/basics.types.html#extended-precision
numpy
long doubles are typically zero padded to 128 bits on 64 bit systems could
that be the reason?

On Mon, 16 Jan 2017 at 07:00 Thomas Caswell  wrote:

> Folks,
>
> Over at h5py we are trying to get a release out and have discovered (via
> debian) that on ppc64el there is an apparent disagreement between the size
> of a native long double according to hdf5 and numpy.
>
> For all of the gorey details see: https://github.com/h5py/h5py/issues/817
>  .
>
> In short, `np.longdouble` seems to be `np.float128` and according to the
> docs should map to the native 'long double'.  However, hdf5 provides a
> `H5T_NATIVE_LDOUBLE` which should also refer to the native 'long double',
> but seems to be a 64 bit float.
>
> Anyone on this list have a ppc64el machine (or experience with) that can
> provide some guidance here?
>
> Tom
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